Telegraphs, track circuits and signals before 1890

2011 ◽  
pp. 1-10
Keyword(s):  
Track Circuits

Study on FSK Signal Demodulation Based on Chirp-Z Transform

2012 ◽  
Vol 532-533 ◽  
pp. 827-831
Author(s):  
Jiang Hua Xia ◽  
Jing Guo ◽  
Xiao Ming Wang
Keyword(s):  
Frequency Shift ◽  
Matlab Simulation ◽  
A Algorithm ◽  
Signal Parameters ◽  
Detailed Explanation ◽  
Symbol Rate ◽  
Shift Keying ◽  
Signal Demodulation ◽  
Track Circuits ◽  
Strong Ability

The Frequency-Shift Keying (FSK) signal is frequently used in track circuits because of the strong ability of resistance interference. Symbol rate is one of the important signal parameters for safety of railway. The paper propose a algorithm of high precise detectation of FSK signal using Chirp-Z transform; Theoretically gives the detailed explanation to the major technique, and has carried on the Matlab simulation, The simulation result indicates this method during the enhancement is shifted signal examination precision, reduced the computation order of complexity.

Study on centre-fed boundless track circuits

2005 ◽  
Vol 152 (5) ◽  
pp. 1049 ◽  
Author(s):  
N. Nikolov ◽  
N. Nedelchev
Keyword(s):  
Track Circuits

Calculation of tonal rail circuits with consideration of a complicated waveform

2021 ◽  
pp. 104-112
Author(s):  
M. I. Shestakov ◽  
Keyword(s):  
Root Mean Square ◽  
Classical Method ◽  
Wide Spectrum ◽  
Discrete Fourier Transformation ◽  
Single Frequency ◽  
Signal Spectrum ◽  
Complex Signal ◽  
Mean Square ◽  
Operating Modes ◽  
Track Circuits

The article deals with the problem of calculating modern audio frequency track circuits, the working signal of which is a frequency-manipulated signal transmitting Bauer codes. Such signals have a wide spectrum, which leads to the error in the calculations performed according to the classical method for a single frequency. Increasing the code transfer rate in order to reduce the response time of track circuits further expands the signal spectrum, which negatively affects the safety of such rail circuits. To determine the width and composition of the signal spectrum, a discrete Fourier transformation is used. The new method of calculating rail circuits proposed in this article allows us to determine the root-mean-square value of the signal at the output of the rail circuit with a known amplitude-frequency characteristic of the rail circuit and the form of a complex signal at the input. The obtained ratio of the root-mean-square signal levels at the input and output of the rail circuit allows us to solve the problem of calculating the operating modes of the rail circuit taking into account its nonlinear amplitude-frequency characteristics and taking into account the complex signal form, including consideration of the specific transmitted Bauer code. This solution allows you to increase the reliability of the calculation of the adjustment tables of the tonal track circuits and, as a result, increase the reliability and safety of their operation.

Clustering Algorithms for Direct Current Track Coded Signals

2019 ◽  
Author(s):  
Song Qin ◽  
Nenad Mijatovic ◽  
Jeffrey Fries ◽  
James Kiss
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
High Availability ◽  
Digital Analysis ◽  
Signaling Systems ◽  
Track Circuits ◽  
Service Conditions ◽  
Track Circuit ◽  
Fail Safe

Designed for detecting train presence on tracks, track circuits must maintain a level of high availability for railway signaling systems. Due to the fail-safe nature of these critical devices, any failures will result in a declaration of occupancy in a section of track which restricts train movements. It is possible to automatically diagnose and, in some cases, predict the failures of track circuits by performing analytics on the track signals. In order to perform these analytics, we need to study the coded signals transmitted to and received from the track. However, these signals consist of heterogeneous pulses that are noisy for data analysis. Thus, we need techniques which will automatically group homogeneous pulses into similar groups. In this paper, we present data cleansing techniques which will cluster pulses based on digital analysis and machine learning. We report the results of our evaluation of clustering algorithms that improve the quality of analytic data. The data were captured under revenue service conditions operated by Alstom. For clustering algorithm, we used the k-means algorithm to cluster heterogeneous pulses. By tailoring the parameters for this algorithm, we can control the pulses of the cluster, allowing for further analysis of the track circuit signals in order to gain insight regarding its performance.

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